On the other hand, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging and one of the bottlenecks lies in the reconstruction algorithm.
These domain-specific optimizations further make way for classical general-purpose optimizations that are originally challenging to directly apply to computations with sparse data structures.
In this paper, we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and 3D viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization.
We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process.
We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.
Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs.
Image segmentation is an important task in many medical applications.
Ranked #1 on Brain Image Segmentation on T1-weighted MRI
We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.
We show that the learned filters achieve high-quality results on real videos, with less ringing artifacts and better noise characteristics than previous methods.
For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms.
How best to evaluate a saliency model's ability to predict where humans look in images is an open research question.